When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models

• URL: http://arxiv.org/abs/2607.06630v1
• Date: Tue, 07 Jul 2026 13:03:45 GMT
• Title: When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models
• Abstract summary: We propose a unified empirical audit framework organized around three failures: verification insufficiency, proxy-fidelity divergence, and latent information exfiltration.<n>We instantiate the framework on BCI Competition IV 2a and SEED-IV using multiple deep and classical EEG decoders, official session-level validation, null controls, and paired statistical tests.<n>Our results establish that operational safety auditing, not certificate verification alone, is necessary for responsible neural-interface deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Formal robustness certificates for embedded neural-interface models can pass while task accuracy collapses: at perturbation budget e=0.25, EEGNet classification accuracy drops by 25.7% under projected-gradient attack while the Lipschitz-style certificate remains valid for all 9 tested subjects. We argue that this gap between mathematical certification and operational safety is one instance of a broader alignment failure in neural interfaces, where training objectives diverge from user welfare. We propose a unified empirical audit framework organized around three such failures: verification insufficiency, in which certificates pass while task behavior degrades; proxy-fidelity divergence, in which task-optimized representations damage neural signal structure (a time-domain auxiliary objective reduces reconstruction MSE by 0.1132 while worsening spectral log-MSE); and latent information exfiltration, in which public-task embeddings retain private attributes (subject identity recoverable at 48.1% versus 6.7% chance). We instantiate the framework on BCI Competition IV 2a and SEED-IV using multiple deep and classical EEG decoders, official session-level validation, null controls, and paired statistical tests. The verification gap persists across EEGNet, CSP+LDA, and FBCSP+LDA, and is therefore architecture-independent. Our results establish that operational safety auditing, not certificate verification alone, is necessary for responsible neural-interface deployment.

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